https://github.com/tomgeorge1234/neurorltutorial

A colab-style tutorial on neuro-reinforcement learning
https://github.com/tomgeorge1234/neurorltutorial

Last synced: 4 months ago
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A colab-style tutorial on neuro-reinforcement learning

• Host: GitHub
• URL: https://github.com/tomgeorge1234/neurorltutorial
• Owner: TomGeorge1234
• Created: 2024-05-24T10:13:57.000Z (about 1 year ago)
• Default Branch: main
• Last Pushed: 2024-06-26T13:27:27.000Z (12 months ago)
• Last Synced: 2025-01-04T14:06:43.397Z (5 months ago)
• Language: Jupyter Notebook
• Size: 31.8 MB
• Stars: 1
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# **Neuro RL** [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/TomGeorge1234/NeuroRLTutorial/blob/main/NeuroRL.ipynb)

## **University of Amsterdam Neuro-AI Summer School, 2024**

### made by: **Tom George (UCL) and Jesse Geerts (Imperial)**

In this tutorial we'll study and build reinforcement learning models inspired by the brain. By the end you'll understand, and be able to construct, a series of simple but surprisingly powerful models of how agents learn to navigate spatial environments and find rewards.

Note: the colab renders better in Safari and Firefox than Chrome.



_Figure 1: An agent has learn to navigate around a wall towards a hidden reward using place cell state features and a simple Q-value learning algorithm._

## Topics covered: 

1. Rescorla-Wagner Model (~60 mins)

2. Temporal Difference Learning (~60 mins)

3. Q-Values and Policy Improvement (~60 mins)

4. State features and function approximation (~60 mins)

## Solutions

Solutions to the maths exercises can be found in a seperate `solutions.ipynb` notebook which may or may not be provided to you by the TAs.